In rolling bearings of pumps and fans that are auxiliary devices used in chemical plants, steel plants, power plants, and other facilities, or in the rolling bearings used in motors for driving these devices, the loads are extremely small, being 5% or less of the rated load, and metal fatigue does not occur therein under normal service conditions. Therefore, the service life of these rolling bearings is affected by two types of failures that include “spalling due to concentrated stress” in portions where there is a buildup of asperities due to contamination with impurities, and “increased vibration” due to an increase in the roughness of the surface of the orbit of the rolling bearing when the lubricating film of grease is broken by moisture contamination.
A variety of methods have been proposed for assessing residual service life of these rolling bearings. Examples include methods whereby the vibration of a bearing is measured using an accelerometer signal, and a warning is issued when the bearing vibration value exceeds an allowable value, such as the “bearing assessment method” of Patent Reference 1; the acoustic emission (Acoustic Emission) method; and other methods. Other methods estimate the cause of a failure by analyzing the frequency of the bearing vibration. Some methods estimate service life by predicting the rate of increase in bearing vibration readings.
Patent Reference 1: Japanese Laid-open Patent Application No. 8-159151.
The most commonly used prediction method is a method for predicting the tendency towards increased bearing vibration using an accelerometer signal. These methods predict the rate of increase in vibration acceleration of bearings through the use of linear, quadratic, and exponential curves, and estimate the remaining service life of the bearings according to the time until the vibration reading reaches a preset allowable vibration value.
For example, as shown in FIG. 12, an effective value from 0 to 10 kHz is computed for an acceleration vibration waveform, and evaluation is performed by measuring two types of threshold values that include an absolute value and a relative value, and determining that the bearing under test is “normal” when the threshold value is not exceeded.
When the threshold value is exceeded, the bearing under test is determined to be “defective,” and the frequency spectrum of the vibration waveform is computed. An n-fold component of the rotational speeds, such as 1N, 2N, 3N, or mN, is extracted.
Alternatively, when a determination is made that the bearing under test is “defective,” then envelope processing of the absolute value and the vibration waveform obtained from LPF processing is performed, and the frequency spectrum of the enveloped waveform is computed. The bearing pass frequency components finn, fout, and fball are then extracted.
The cause of the defect is estimated with consideration for the size of the vibration components on the basis of these measurement results. Causes for defects include unbalance or misalignment of rolling bearings, looseness in the substructure, and other factors.
The acoustic emission method is a method of predicting residual service life that utilizes AE signals in a frequency higher than the acceleration to discover early-stage rolling bearing defects. The AE method is a method of prediction that uses AE signals, which are created when built-up strain energy is released in the form of sound as solid objects undergo deformation or breakdown. These AE signals, which are transmissions of elastic waves, are released when elastic energy is released from inside a material, not necessarily only during physical breakdown, but also when dislocation or transformation of crystal structures in a material occurs. The AE signals are processed through the use of an AE sensor while the rolling bearing is in operation, and predictions can be made regarding the rolling bearing by observing how often AE waves occur.
Using these kinds of prediction methods, unanticipated rolling bearing failures can be predicted before they actually happen, and intervals for replacing affected bearings can be estimated in advance. Thus, the “normal operation life,” during which detection of irregularities in bearings occurs, and the “defective life”, during which bearing overheating and fracturing occur, can be clearly defined, and the interval between the normal operation life and defective life, i.e., the residual service life, can be predicted. In the past, the timing of repairs performed on a rolling bearing was determined by determining the presence of an abnormality in a rotating machine, estimating the cause of the abnormality, and determining the severity of the abnormality. The most common kinds of statistical prediction for vibration acceleration utilize, as a parameter, readings of vibrations until fulfillment of the predicted service life, and carry out curvilinear regression using quadratic and exponential curves so as to define residual service life as the period until vibration readings reach an allowable vibration value.